Trung Le - RankMe

236 papers A* 49A 11B 23C 4Journal 115Unranked 34

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Antibody: Strengthening Defense Against Harmful Fine-Tuning for Large Language Models via Attenuating Harmful Gradient Influence. CoRR Quoc Minh Nguyen, Trung Le, Jing Wu, Anh Tuan Bui, Mehrtash Harandi
2026	—	`conf`	Beyond Coherence: Improving Temporal Consistency and Interpretability in Dynamic Topic Models. EACL (Findings) Thanh Vinh Nguyen, Ngo Van Dong, Minh Chu Xuan, Tung Nguyen, Linh Ngo Van, Dinh Viet Sang, Trung Le
2026	A*	`conf`	CTPD: Cross Tokenizer Preference Distillation. AAAI Truong Nguyen, Phi Van Dat, Ngan Nguyen, Linh Ngo Van, Trung Le, Thanh Hong Nguyen
2026	J	`jnl`	CTPD: Cross Tokenizer Preference Distillation. CoRR Truong Nguyen, Phi Van Dat, Ngan Nguyen, Linh Ngo Van, Trung Le, Thanh Hong Nguyen
2026	—	`conf`	Causal Direct Preference Optimization for Language Model Alignment. EACL (Findings) Uyen Le, Thin Nguyen, Toan Nguyen, Toan Doan, Trung Le, Bac Le
2026	A*	`conf`	DIET: Machine Unlearning on a Data-Diet. AAAI Nilakshan Kunananthaseelan, Jing Wu, Trung Le, Gholamreza Haffari, Mehrtash Harandi
2026	—	`conf`	DWA-KD: Dual-Space Weighting and Time-Warped Alignment for Cross-Tokenizer Knowledge Distillation. EACL (Findings) Duc Trung Vu, Chi Pham Khanh, Phi Van Dat, Ngo Van Linh, Dinh Viet Sang, Trung Le
2026	J	`jnl`	DWA-KD: Dual-Space Weighting and Time-Warped Alignment for Cross-Tokenizer Knowledge Distillation. CoRR Duc-Quang Vu, Chi Pham Khanh, Phi Van Dat, Ngo Van Linh, Sang Dinh, Trung Le
2026	J	`jnl`	Efficient Temporal-aware Matryoshka Adaptation for Temporal Information Retrieval. CoRR Tuan-Luc Huynh, Weiqing Wang, Trung Le, Thuy-Trang Vu, Dragan Gasevic, Yuan-Fang Li, Thanh-Toan Do
2026	A*	`conf`	MCW-KD: Multi-Cost Wasserstein Knowledge Distillation for Large Language Models. AAAI Hoang Tran Vuong, Tue Le, Quyen Tran, Linh Ngo Van, Trung Le
2026	J	`jnl`	Ordering-based Causal Discovery via Generalized Score Matching. CoRR Vy Vo, He Zhao, Trung Le, Edwin V. Bonilla, Dinh Phung
2026	J	`jnl`	Sharpness-Aware Minimization in Logit Space Efficiently Enhances Direct Preference Optimization. CoRR Haocheng Luo, Zehang Deng, Thanh-Toan Do, Mehrtash Harandi, Dinh Q. Phung, Trung Le
2026	J	`jnl`	Test-Time Instance-Specific Parameter Composition: A New Paradigm for Adaptive Generative Modeling. CoRR Minh-Tuan Tran, Xuan-May Le, Quan Hung Tran, Mehrtash Harandi, Dinh Q. Phung, Trung Le
2025	J	`jnl`	Adaptive Layer-Wise Transformations for Post-Training Quantization of Large Language Models. CoRR Cuong Pham, Hoang Anh Dung, Cuong C. Nguyen, Trung Le, Gustavo Carneiro, Jianfei Cai, Thanh-Toan Do
2025	A*	`conf`	Boosting Multiple Views for pretrained-based Continual Learning. ICLR Quyen Tran, Tung Lam Tran, Khanh Doan, Toan Tran, Dinh Q. Phung, Khoat Than, Trung Le
2025	J	`jnl`	CoT2Align: Cross-Chain of Thought Distillation via Optimal Transport Alignment for Language Models with Different Tokenizers. CoRR Anh Duc Le, Tu Vu, Nam Le Hai, Nguyen Thi Ngoc Diep, Linh Ngo Van, Trung Le, Thien Huu Nguyen
2025	J	`jnl`	DAViT: A Domain-Adapted Vision Transformer for Automated Pneumonia Detection and Explanation Using Chest X-Ray Images. IEEE Access Michael Fu, Chakkrit Tantithamthavorn, Trung Le
2025	J	`jnl`	DeepVulMatch: Learning and Matching Latent Vulnerability Representations for Dual-Granularity Vulnerability Detection. IEEE Trans. Reliab. Michael Fu, Trung Le, Van Nguyen, Chakkrit Tantithamthavorn, Dinh Q. Phung
2025	A*	`conf`	EMO: Embedding Model Distillation via Intra-Model Relation and Optimal Transport Alignments. EMNLP Minh-Phuc Truong, Hai An Vu, Tu Vu, Nguyen Thi Ngoc Diep, Linh Van Ngo, Thien Huu Nguyen, Trung Le
2025	A*	`conf`	Enhancing Dataset Distillation via Non-Critical Region Refinement. CVPR Minh-Tuan Tran, Trung Le, Xuan-May Le, Thanh-Toan Do, Dinh Q. Phung
2025	J	`jnl`	Enhancing Dataset Distillation via Non-Critical Region Refinement. CoRR Minh-Tuan Tran, Trung Le, Xuan-May Le, Thanh-Toan Do, Dinh Phung
2025	A*	`conf`	Erasing Undesirable Influence in Diffusion Models. CVPR Jing Wu, Trung Le, Munawar Hayat, Mehrtash Harandi
2025	J	`jnl`	Exemplar-Free Continual Learning for State Space Models. CoRR Isaac Ning Lee, Leila Mahmoodi, Trung Le, Mehrtash Harandi
2025	J	`jnl`	Explicit Eigenvalue Regularization Improves Sharpness-Aware Minimization. CoRR Haocheng Luo, Tuan Truong, Tung Pham, Mehrtash Harandi, Dinh Q. Phung, Trung Le
2025	A*	`conf`	Fantastic Targets for Concept Erasure in Diffusion Models and Where To Find Them. ICLR Anh Tuan Bui, Thuy-Trang Vu, Long Tung Vuong, Trung Le, Paul Montague, Tamas Abraham, Junae Kim, Dinh Phung
2025	J	`jnl`	Fantastic Targets for Concept Erasure in Diffusion Models and Where To Find Them. CoRR Anh Bui, Trang Vu, Tung-Long Vuong, Trung Le, Paul Montague, Tamas Abraham, Junae Kim, Dinh Q. Phung
2025	J	`jnl`	HVQ-VAE: Variational auto-encoder with hyperbolic vector quantization. Comput. Vis. Image Underst. Shangyu Chen, Pengfei Fang, Mehrtash Harandi, Trung Le, Jianfei Cai, Dinh Q. Phung
2025	A*	`conf`	Improved Training Technique for Latent Consistency Models. ICLR Quan Dao, Khanh Doan, Di Liu, Trung Le, Dimitris N. Metaxas
2025	J	`jnl`	Improved Training Technique for Latent Consistency Models. CoRR Quan Dao, Khanh Doan, Di Liu, Trung Le, Dimitris N. Metaxas
2025	A*	`conf`	Improving Generalization with Flat Hilbert Bayesian Inference. ICML Tuan Truong, Quyen Tran, Ngoc-Quan Pham, Nhat Ho, Dinh Phung, Trung Le
2025	J	`jnl`	Layer-Wise High-Impact Parameter Ratio Optimization in Post-Training Quantization for Large Language Models. CoRR Cuong Pham, Hoang Anh Dung, Cuong C. Nguyen, Trung Le, Gustavo Carneiro, Thanh-Toan Do
2025	J	`jnl`	Mitigating Semantic Collapse in Generative Personalization with a Surprisingly Simple Test-Time Embedding Adjustment. CoRR Anh Bui, Trang Vu, Trung Le, Junae Kim, Tamas Abraham, Rollin Omari, Amar Kaur, Dinh Q. Phung
2025	A*	`conf`	MixLoRA-DSI: Dynamically Expandable Mixture-of-LoRA Experts for Rehearsal-Free Generative Retrieval over Dynamic Corpora. EMNLP Tuan-Luc Huynh, Thuy-Trang Vu, Weiqing Wang, Trung Le, Dragan Gasevic, Yuan-Fang Li, Thanh-Toan Do
2025	J	`jnl`	MixLoRA-DSI: Dynamically Expandable Mixture-of-LoRA Experts for Rehearsal-Free Generative Retrieval over Dynamic Corpora. CoRR Tuan-Luc Huynh, Thuy-Trang Vu, Weiqing Wang, Trung Le, Dragan Gasevic, Yuan-Fang Li, Thanh-Toan Do
2025	—	`conf`	Multi-Surrogate-Objective Optimization for Neural Topic Models. EMNLP (Findings) Tue Le, Hoang Tran Vuong, Tung Nguyen, Linh Ngo Van, Dinh Viet Sang, Trung Le, Thien Huu Nguyen
2025	—	`conf`	Mutual-pairing Data Augmentation for Fewshot Continual Relation Extraction. NAACL (Long Papers) Nguyen Hoang Anh, Quyen Tran, Thanh Xuan Nguyen, Nguyen Thi Ngoc Diep, Linh Ngo Van, Thien Huu Nguyen, Trung Le
2025	J	`jnl`	On the Mechanisms of Collaborative Learning in VAE Recommenders. CoRR Tung-Long Vuong, Julien Monteil, Hien Dang, Volodymyr Vaskovych, Trung Le, Vu Nguyen
2025	J	`jnl`	Optimizing Specific and Shared Parameters for Efficient Parameter Tuning. CoRR Van-Anh Nguyen, Thanh-Toan Do, Mehrtash Harandi, Dinh Q. Phung, Trung Le
2025	A*	`conf`	Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation. CVPR Tung-Long Vuong, Hoang Phan, Vy Vo, Anh Bui, Thanh-Toan Do, Trung Le, Dinh Phung
2025	J	`jnl`	Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation. CoRR Tung-Long Vuong, Hoang Phan, Vy Vo, Anh Bui, Thanh-Toan Do, Trung Le, Dinh Q. Phung
2025	A*	`conf`	Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models. ICML Ngoc-Quan Pham, Tuan Truong, Quyen Tran, Tan Minh Nguyen, Dinh Phung, Trung Le
2025	J	`jnl`	Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models. CoRR Ngoc-Quan Pham, Tuan Truong, Quyen Tran, Tan Nguyen, Dinh Q. Phung, Trung Le
2025	—	`conf`	PromptDSI: Prompt-Based Rehearsal-Free Continual Learning for Document Retrieval. ECML/PKDD (7) Tuan-Luc Huynh, Thuy-Trang Vu, Weiqing Wang, Yinwei Wei, Trung Le, Dragan Gasevic, Yuan-Fang Li, Thanh-Toan Do
2025	J	`jnl`	RepLoRA: Reparameterizing Low-Rank Adaptation via the Perspective of Mixture of Experts. CoRR Tuan Truong, Chau Nguyen, Huy Nguyen, Le Minh Nguyen, Trung Le, Nhat Ho
2025	A*	`conf`	RepLoRA: Reparameterizing Low-rank Adaptation via the Perspective of Mixture of Experts. ICML Tuan Truong, Chau Nguyen, Huy Nguyen, Minh Le, Trung Le, Nhat Ho
2025	J	`jnl`	Rethinking Output Alignment For 1-bit Post-Training Quantization of Large Language Models. CoRR Dung Anh Hoang, Cuong Pham, Cuong Nguyen, Trung Le, Jianfei Cai, Thanh-Toan Do
2025	A*	`conf`	Revisiting Prefix-tuning: Statistical Benefits of Reparameterization among Prompts. ICLR Minh Le, Chau Nguyen, Huy Nguyen, Quyen Tran, Trung Le, Nhat Ho
2025	J	`jnl`	Token-Regulated Group Relative Policy Optimization for Stable Reinforcement Learning in Large Language Models. CoRR Tue Le, Nghi D. Q. Bui, Linh Ngo Van, Trung Le
2025	J	`jnl`	Unveiling m-Sharpness Through the Structure of Stochastic Gradient Noise. CoRR Haocheng Luo, Mehrtash Harandi, Dinh Phung, Trung Le
2025	J	`jnl`	Why Domain Generalization Fail? A View of Necessity and Sufficiency. CoRR Long Tung Vuong, Vy Vo, Hien Dang, Van-Anh Nguyen, Thanh-Toan Do, Mehrtash Harandi, Trung Le, Dinh Q. Phung
2025	—	`conf`	XTRA: Cross-Lingual Topic Modeling with Topic and Representation Alignments. EMNLP (Findings) Tien-Phat Nguyen, Vu Minh Ngo, Tung Nguyen, Linh Ngo Van, Duc Anh Nguyen, Dinh Viet Sang, Trung Le
2025	J	`jnl`	XTRA: Cross-Lingual Topic Modeling with Topic and Representation Alignments. CoRR Tien-Phat Nguyen, Vu Minh Ngo, Tung Nguyen, Linh Van Ngo, Duc Anh Nguyen, Sang Dinh, Trung Le
2024	J	`jnl`	A Class-aware Optimal Transport Approach with Higher-Order Moment Matching for Unsupervised Domain Adaptation. CoRR Tuan Nguyen, Van Nguyen, Trung Le, He Zhao, Quan Hung Tran, Dinh Q. Phung
2024	J	`jnl`	AIBugHunter: A Practical tool for predicting, classifying and repairing software vulnerabilities. Empir. Softw. Eng. Michael Fu, Chakkrit Tantithamthavorn, Trung Le, Yuki Kume, Van Nguyen, Dinh Q. Phung, John C. Grundy
2024	J	`jnl`	Agnostic Sharpness-Aware Minimization. CoRR Van-Anh Nguyen, Quyen Tran, Tuan Truong, Thanh-Toan Do, Dinh Quoc Phung, Trung Le
2024	J	`jnl`	Connective Viewpoints of Signal-to-Noise Diffusion Models. CoRR Khanh Doan, Long Tung Vuong, Tuan Nguyen, Anh Tuan Bui, Quyen Tran, Thanh-Toan Do, Dinh Phung, Trung Le
2024	J	`jnl`	Deep Domain Adaptation With Max-Margin Principle for Cross-Project Imbalanced Software Vulnerability Detection. ACM Trans. Softw. Eng. Methodol. Van Nguyen, Trung Le, Chakkrit Tantithamthavorn, John C. Grundy, Dinh Q. Phung
2024	—	`conf`	DiffAugment: Diffusion Based Long-Tailed Visual Relationship Recognition. ECCV Workshops (20) Parul Gupta, Tuan Nguyen, Abhinav Dhall, Munawar Hayat, Trung Le, Thanh-Toan Do
2024	J	`jnl`	DiffAugment: Diffusion based Long-Tailed Visual Relationship Recognition. CoRR Parul Gupta, Tuan Nguyen, Abhinav Dhall, Munawar Hayat, Trung Le, Thanh-Toan Do
2024	J	`jnl`	Diversity-Aware Agnostic Ensemble of Sharpness Minimizers. CoRR Anh Bui, Vy Vo, Tung Pham, Dinh Q. Phung, Trung Le
2024	A*	`conf`	Enhancing Domain Adaptation through Prompt Gradient Alignment. NeurIPS Viet Hoang Phan, Tung Lam Tran, Quyen Tran, Trung Le
2024	J	`jnl`	Enhancing Domain Adaptation through Prompt Gradient Alignment. CoRR Hoang Phan, Lam Tran, Quyen Tran, Trung Le
2024	J	`jnl`	EraseDiff: Erasing Data Influence in Diffusion Models. CoRR Jing Wu, Trung Le, Munawar Hayat, Mehrtash Harandi
2024	A*	`conf`	Erasing Undesirable Concepts in Diffusion Models with Adversarial Preservation. NeurIPS Anh Bui, Tung-Long Vuong, Khanh Doan, Trung Le, Paul Montague, Tamas Abraham, Dinh Q. Phung
2024	J	`jnl`	Erasing Undesirable Concepts in Diffusion Models with Adversarial Preservation. CoRR Anh Bui, Long Tung Vuong, Khanh Doan, Trung Le, Paul Montague, Tamas Abraham, Dinh Q. Phung
2024	A*	`conf`	Explicit Eigenvalue Regularization Improves Sharpness-Aware Minimization. NeurIPS Haocheng Luo, Tuan Truong, Tung Pham, Mehrtash Harandi, Dinh Q. Phung, Trung Le
2024	A	`conf`	Frequency Attention for Knowledge Distillation. WACV Cuong Pham, Van-Anh Nguyen, Trung Le, Dinh Q. Phung, Gustavo Carneiro, Thanh-Toan Do
2024	J	`jnl`	Frequency Attention for Knowledge Distillation. CoRR Cuong Pham, Van-Anh Nguyen, Trung Le, Dinh Q. Phung, Gustavo Carneiro, Thanh-Toan Do
2024	J	`jnl`	Improving Generalization with Flat Hilbert Bayesian Inference. CoRR Tuan Truong, Quyen Tran, Quan Pham-Ngoc, Nhat Ho, Dinh Phung, Trung Le
2024	J	`jnl`	Large-Scale Data-Free Knowledge Distillation for ImageNet via Multi-Resolution Data Generation. CoRR Minh-Tuan Tran, Trung Le, Xuan-May Le, Jianfei Cai, Mehrtash Harandi, Dinh Q. Phung
2024	J	`jnl`	Leveraging Hierarchical Taxonomies in Prompt-based Continual Learning. CoRR Quyen Tran, Minh Le, Tuan Truong, Dinh Phung, Linh Ngo Van, Thien Huu Nguyen, Nhat Ho, Trung Le
2024	J	`jnl`	MetaAug: Meta-Data Augmentation for Post-Training Quantization. CoRR Cuong Pham, Hoang Anh Dung, Cuong C. Nguyen, Trung Le, Dinh Phung, Gustavo Carneiro, Thanh-Toan Do
2024	—	`conf`	MetaAug: Meta-data Augmentation for Post-training Quantization. ECCV (27) Cuong Pham, Hoang Anh Dung, Cuong C. Nguyen, Trung Le, Dinh Phung, Gustavo Carneiro, Thanh-Toan Do
2024	J	`jnl`	Model and Feature Diversity for Bayesian Neural Networks in Mutual Learning. CoRR Cuong Pham, Cuong C. Nguyen, Trung Le, Dinh Q. Phung, Gustavo Carneiro, Thanh-Toan Do
2024	A*	`conf`	NAYER: Noisy Layer Data Generation for Efficient and Effective Data-free Knowledge Distillation. CVPR Minh-Tuan Tran, Trung Le, Xuan-May Le, Mehrtash Harandi, Quan Hung Tran, Dinh Q. Phung
2024	A*	`conf`	Optimal Transport for Structure Learning Under Missing Data. ICML Vy Vo, He Zhao, Trung Le, Edwin V. Bonilla, Dinh Phung
2024	J	`jnl`	Optimal Transport for Structure Learning Under Missing Data. CoRR Vy Vo, He Zhao, Trung Le, Edwin V. Bonilla, Dinh Q. Phung
2024	A*	`conf`	Parameter Estimation in DAGs from Incomplete Data via Optimal Transport. ICML Vy Vo, Trung Le, Long Tung Vuong, He Zhao, Edwin V. Bonilla, Dinh Phung
2024	A*	`conf`	Preserving Generalization of Language models in Few-shot Continual Relation Extraction. EMNLP Quyen Tran, Nguyen Xuan Thanh, Nguyen Hoang Anh, Nam Le Hai, Trung Le, Linh Van Ngo, Thien Huu Nguyen
2024	J	`jnl`	Preserving Generalization of Language models in Few-shot Continual Relation Extraction. CoRR Quyen Tran, Nguyen Xuan Thanh, Nguyen Hoang Anh, Nam Le Hai, Trung Le, Linh Ngo Van, Thien Huu Nguyen
2024	J	`jnl`	PromptDSI: Prompt-based Rehearsal-free Instance-wise Incremental Learning for Document Retrieval. CoRR Tuan-Luc Huynh, Thuy-Trang Vu, Weiqing Wang, Yinwei Wei, Trung Le, Dragan Gasevic, Yuan-Fang Li, Thanh-Toan Do
2024	J	`jnl`	Removing Undesirable Concepts in Text-to-Image Generative Models with Learnable Prompts. CoRR Anh Tuan Bui, Khanh Doan, Trung Le, Paul Montague, Tamas Abraham, Dinh Q. Phung
2024	J	`jnl`	Revisiting Prefix-tuning: Statistical Benefits of Reparameterization among Prompts. CoRR Minh Le, Chau Nguyen, Huy Nguyen, Quyen Tran, Trung Le, Nhat Ho
2024	A*	`conf`	Sharpness-Aware Data Generation for Zero-shot Quantization. ICML Hoang Anh Dung, Cuong Pham, Trung Le, Jianfei Cai, Thanh-Toan Do
2024	A*	`conf`	Text-Enhanced Data-Free Approach for Federated Class-Incremental Learning. CVPR Minh-Tuan Tran, Trung Le, Xuan-May Le, Mehrtash Harandi, Dinh Phung
2024	J	`jnl`	Text-Enhanced Data-free Approach for Federated Class-Incremental Learning. CoRR Minh-Tuan Tran, Trung Le, Xuan-May Le, Mehrtash Harandi, Dinh Phung
2024	J	`jnl`	Vision Transformer Inspired Automated Vulnerability Repair. ACM Trans. Softw. Eng. Methodol. Michael Fu, Van Nguyen, Chakkrit Tantithamthavorn, Dinh Phung, Trung Le
2023	J	`jnl`	AIBugHunter: A Practical Tool for Predicting, Classifying and Repairing Software Vulnerabilities. CoRR Michael Fu, Chakkrit Tantithamthavorn, Trung Le, Yuki Kume, Van Nguyen, Dinh Phung, John C. Grundy
2023	A	`conf`	Adversarial local distribution regularization for knowledge distillation. WACV Thanh Nguyen-Duc, Trung Le, He Zhao, Jianfei Cai, Dinh Phung
2023	A*	`conf`	An Additive Instance-Wise Approach to Multi-class Model Interpretation. ICLR Vy Vo, Van Nguyen, Trung Le, Quan Hung Tran, Gholamreza Haffari, Seyit Camtepe, Dinh Phung
2023	C	`conf`	ChatGPT for Vulnerability Detection, Classification, and Repair: How Far Are We? APSEC Michael Fu, Chakkrit Kla Tantithamthavorn, Van Nguyen, Trung Le
2023	J	`jnl`	ChatGPT for Vulnerability Detection, Classification, and Repair: How Far Are We? CoRR Michael Fu, Chakkrit Tantithamthavorn, Van Nguyen, Trung Le
2023	J	`jnl`	Class-Prototype Conditional Diffusion Model for Continual Learning with Generative Replay. CoRR Khanh Doan, Quyen Tran, Tuan Nguyen, Dinh Q. Phung, Trung Le
2023	—	`conf`	Cross-Adversarial Local Distribution Regularization for Semi-supervised Medical Image Segmentation. MICCAI (1) Thanh Nguyen-Duc, Trung Le, Roland Bammer, He Zhao, Jianfei Cai, Dinh Q. Phung
2023	J	`jnl`	Cross-adversarial local distribution regularization for semi-supervised medical image segmentation. CoRR Thanh Nguyen-Duc, Trung Le, Roland Bammer, He Zhao, Jianfei Cai, Dinh Q. Phung
2023	A*	`conf`	Feature-based Learning for Diverse and Privacy-Preserving Counterfactual Explanations. KDD Vy Vo, Trung Le, Van Nguyen, He Zhao, Edwin V. Bonilla, Gholamreza Haffari, Dinh Q. Phung
2023	A*	`conf`	Flat Seeking Bayesian Neural Networks. NeurIPS Van-Anh Nguyen, Tung-Long Vuong, Hoang Phan, Thanh-Toan Do, Dinh Q. Phung, Trung Le
2023	J	`jnl`	Flat Seeking Bayesian Neural Networks. CoRR Van-Anh Nguyen, Long Tung Vuong, Hoang Phan, Thanh-Toan Do, Dinh Q. Phung, Trung Le
2023	J	`jnl`	Generating Adversarial Examples with Task Oriented Multi-Objective Optimization. CoRR Anh Tuan Bui, Trung Le, He Zhao, Quan Hung Tran, Paul Montague, Dinh Q. Phung
2023	J	`jnl`	Generating Adversarial Examples with Task Oriented Multi-Objective Optimization. Trans. Mach. Learn. Res. Anh Tuan Bui, Trung Le, He Zhao, Quan Hung Tran, Paul Montague, Dinh Phung
2023	A	`conf`	Global-Local Regularization Via Distributional Robustness. AISTATS Hoang Phan, Trung Le, Trung Phung, Anh Tuan Bui, Nhat Ho, Dinh Q. Phung
2023	J	`jnl`	Hyperbolic Geometry in Computer Vision: A Survey. CoRR Pengfei Fang, Mehrtash Harandi, Trung Le, Dinh Q. Phung
2023	J	`jnl`	KOPPA: Improving Prompt-based Continual Learning with Key-Query Orthogonal Projection and Prototype-based One-Versus-All. CoRR Quyen Tran, Lam Tran, Khoat Than, Toan Tran, Dinh Q. Phung, Trung Le
2023	J	`jnl`	Learning Directed Graphical Models with Optimal Transport. CoRR Vy Vo, Trung Le, Long Tung Vuong, He Zhao, Edwin V. Bonilla, Dinh Q. Phung
2023	J	`jnl`	Learning to Quantize Vulnerability Patterns and Match to Locate Statement-Level Vulnerabilities. CoRR Michael Fu, Trung Le, Van Nguyen, Chakkrit Tantithamthavorn, Dinh Q. Phung
2023	A*	`conf`	Model and Feature Diversity for Bayesian Neural Networks in Mutual Learning. NeurIPS Van Cuong Pham, Cuong C. Nguyen, Trung Le, Dinh Phung, Gustavo Carneiro, Thanh-Toan Do
2023	A*	`conf`	Optimal Transport Model Distributional Robustness. NeurIPS Van-Anh Nguyen, Trung Le, Anh Tuan Bui, Thanh-Toan Do, Dinh Q. Phung
2023	J	`jnl`	Optimal Transport Model Distributional Robustness. CoRR Van-Anh Nguyen, Trung Le, Anh Tuan Bui, Thanh-Toan Do, Dinh Q. Phung
2023	J	`jnl`	RSAM: Learning on manifolds with Riemannian Sharpness-aware Minimization. CoRR Tuan Truong, Hoang-Phi Nguyen, Tung Pham, Minh-Tuan Tran, Mehrtash Harandi, Dinh Phung, Trung Le
2023	J	`jnl`	Robust Contrastive Learning With Theory Guarantee. CoRR Ngoc N. Tran, Lam Tran, Hoang Phan, Anh Tuan Bui, Tung Pham, Toan Tran, Dinh Q. Phung, Trung Le
2023	J	`jnl`	Sharpness & Shift-Aware Self-Supervised Learning. CoRR Ngoc N. Tran, Son Duong, Hoang Phan, Tung Pham, Dinh Q. Phung, Trung Le
2023	J	`jnl`	Unleash Data Generation for Efficient and Effective Data-free Knowledge Distillation. CoRR Minh-Tuan Tran, Trung Le, Xuan-May Le, Mehrtash Harandi, Quan Hung Tran, Dinh Q. Phung
2023	A*	`conf`	Vector Quantized Wasserstein Auto-Encoder. ICML Long Tung Vuong, Trung Le, He Zhao, Chuanxia Zheng, Mehrtash Harandi, Jianfei Cai, Dinh Q. Phung
2023	J	`jnl`	Vector Quantized Wasserstein Auto-Encoder. CoRR Tung-Long Vuong, Trung Le, He Zhao, Chuanxia Zheng, Mehrtash Harandi, Jianfei Cai, Dinh Q. Phung
2023	J	`jnl`	VulExplainer: A Transformer-Based Hierarchical Distillation for Explaining Vulnerability Types. IEEE Trans. Software Eng. Michael Fu, Van Nguyen, Chakkrit Kla Tantithamthavorn, Trung Le, Dinh Q. Phung
2022	A*	`conf`	A Unified Wasserstein Distributional Robustness Framework for Adversarial Training. ICLR Anh Tuan Bui, Trung Le, Quan Hung Tran, He Zhao, Dinh Q. Phung
2022	J	`jnl`	A Unified Wasserstein Distributional Robustness Framework for Adversarial Training. CoRR Tuan-Anh Bui, Trung Le, Quan Hung Tran, He Zhao, Dinh Q. Phung
2022	J	`jnl`	An Additive Instance-Wise Approach to Multi-class Model Interpretation. CoRR Vy Vo, Van Nguyen, Trung Le, Quan Hung Tran, Gholamreza Haffari, Seyit Camtepe, Dinh Q. Phung
2022	J	`jnl`	An Information-Theoretic and Contrastive Learning-based Approach for Identifying Code Statements Causing Software Vulnerability. CoRR Van Nguyen, Trung Le, Chakkrit Tantithamthavorn, John C. Grundy, Hung Nguyen, Seyit Camtepe, Paul Quirk, Dinh Q. Phung
2022	J	`jnl`	Continual Learning with Optimal Transport based Mixture Model. CoRR Quyen Tran, Hoang Phan, Khoat Than, Dinh Q. Phung, Trung Le
2022	J	`jnl`	Cross Project Software Vulnerability Detection via Domain Adaptation and Max-Margin Principle. CoRR Van Nguyen, Trung Le, Chakkrit Tantithamthavorn, John C. Grundy, Hung Nguyen, Dinh Q. Phung
2022	A	`conf`	Cycle class consistency with distributional optimal transport and knowledge distillation for unsupervised domain adaptation. UAI Tuan Nguyen, Van Nguyen, Trung Le, He Zhao, Quan Hung Tran, Dinh Q. Phung
2022	J	`jnl`	Global-Local Regularization Via Distributional Robustness. CoRR Hoang Phan, Trung Le, Trung Phung, Tuan-Anh Bui, Nhat Ho, Dinh Q. Phung
2022	J	`jnl`	Improving Multi-task Learning via Seeking Task-based Flat Regions. CoRR Hoang Phan, Lam Tran, Ngoc N. Tran, Nhat Ho, Dinh Q. Phung, Trung Le
2022	J	`jnl`	Improving kernel online learning with a snapshot memory. Mach. Learn. Trung Le, Khanh Nguyen, Dinh Q. Phung
2022	J	`jnl`	Learning to Counter: Stochastic Feature-based Learning for Diverse Counterfactual Explanations. CoRR Vy Vo, Trung Le, Van Nguyen, He Zhao, Edwin V. Bonilla, Gholamreza Haffari, Dinh Q. Phung
2022	J	`jnl`	Multiple Perturbation Attack: Attack Pixelwise Under Different $\ell_p$-norms For Better Adversarial Performance. CoRR Ngoc N. Tran, Anh Tuan Bui, Dinh Q. Phung, Trung Le
2022	A	`conf`	On Global-view Based Defense via Adversarial Attack and Defense Risk Guaranteed Bounds. AISTATS Trung Le, Anh Tuan Bui, Le Minh Tri Tue, He Zhao, Paul Montague, Quan Hung Tran, Dinh Q. Phung
2022	A*	`conf`	On Transportation of Mini-batches: A Hierarchical Approach. ICML Khai Nguyen, Dang Nguyen, Quoc Dinh Nguyen, Tung Pham, Hung Bui, Dinh Phung, Trung Le, Nhat Ho
2022	A	`conf`	Particle-based Adversarial Local Distribution Regularization. AISTATS Thanh Nguyen-Duc, Trung Le, He Zhao, Jianfei Cai, Dinh Q. Phung
2022	—	`conf`	ReGVD: Revisiting Graph Neural Networks for Vulnerability Detection. ICSE-Companion Van-Anh Nguyen, Dai Quoc Nguyen, Van Nguyen, Trung Le, Quan Hung Tran, Dinh Phung
2022	J	`jnl`	Robust Variational Learning for Multiclass Kernel Models With Stein Refinement. IEEE Trans. Knowl. Data Eng. Khanh Nguyen, Trung Le, Tu Dinh Nguyen, Geoffrey I. Webb, Dinh Phung
2022	A*	`conf`	Stochastic Multiple Target Sampling Gradient Descent. NeurIPS Hoang Phan, Ngoc Tran, Trung Le, Toan Tran, Nhat Ho, Dinh Phung
2022	J	`jnl`	Stochastic Multiple Target Sampling Gradient Descent. CoRR Hoang Phan, Ngoc Tran, Trung Le, Toan Tran, Nhat Ho, Dinh Phung
2022	J	`jnl`	Vision Transformer Visualization: What Neurons Tell and How Neurons Behave? CoRR Van-Anh Nguyen, Khanh Pham Dinh, Long Tung Vuong, Thanh-Toan Do, Quan Hung Tran, Dinh Q. Phung, Trung Le
2022	—	`conf`	VulRepair: a T5-based automated software vulnerability repair. ESEC/SIGSOFT FSE Michael Fu, Chakkrit Tantithamthavorn, Trung Le, Van Nguyen, Dinh Q. Phung
2021	J	`jnl`	BoMb-OT: On Batch of Mini-batches Optimal Transport. CoRR Khai Nguyen, Quoc Nguyen, Nhat Ho, Tung Pham, Hung Bui, Dinh Phung, Trung Le
2021	J	`jnl`	Improved and Efficient Text Adversarial Attacks using Target Information. CoRR Mahmoud Hossam, Trung Le, He Zhao, Viet Huynh, Dinh Phung
2021	A*	`conf`	Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial Robustness. AAAI Tuan-Anh Bui, Trung Le, He Zhao, Paul Montague, Olivier Y. de Vel, Tamas Abraham, Dinh Phung
2021	B	`conf`	Information-theoretic Source Code Vulnerability Highlighting. IJCNN Van Nguyen, Trung Le, Olivier Y. de Vel, Paul Montague, John Grundy, Dinh Phung
2021	A*	`conf`	LAMDA: Label Matching Deep Domain Adaptation. ICML Trung Le, Tuan Nguyen, Nhat Ho, Hung Bui, Dinh Phung
2021	A	`conf`	Most: multi-source domain adaptation via optimal transport for student-teacher learning. UAI Tuan Nguyen, Trung Le, He Zhao, Quan Hung Tran, Truyen Nguyen, Dinh Q. Phung
2021	A*	`conf`	Neural Topic Model via Optimal Transport. ICLR He Zhao, Dinh Phung, Viet Huynh, Trung Le, Wray L. Buntine
2021	J	`jnl`	On Label Shift in Domain Adaptation via Wasserstein Distance. CoRR Trung Le, Dat Do, Tuan Nguyen, Huy Nguyen, Hung Bui, Nhat Ho, Dinh Q. Phung
2021	A*	`conf`	On Learning Domain-Invariant Representations for Transfer Learning with Multiple Sources. NeurIPS Trung Phung, Trung Le, Long Vuong, Toan Tran, Anh Tran, Hung Bui, Dinh Q. Phung
2021	J	`jnl`	On Learning Domain-Invariant Representations for Transfer Learning with Multiple Sources. CoRR Trung Phung, Trung Le, Long Vuong, Toan Tran, Anh Tran, Hung Bui, Dinh Phung
2021	J	`jnl`	ReGVD: Revisiting Graph Neural Networks for Vulnerability Detection. CoRR Van-Anh Nguyen, Dai Quoc Nguyen, Van Nguyen, Trung Le, Quan Hung Tran, Dinh Q. Phung
2021	A*	`conf`	STEM: An approach to Multi-source Domain Adaptation with Guarantees. ICCV Van-Anh Nguyen, Tuan Nguyen, Trung Le, Quan Hung Tran, Dinh Phung
2021	A*	`conf`	TIDOT: A Teacher Imitation Learning Approach for Domain Adaptation with Optimal Transport. IJCAI Tuan Nguyen, Trung Le, Nhan Dam, Quan Hung Tran, Truyen Nguyen, Dinh Q. Phung
2021	J	`jnl`	Text Generation with Deep Variational GAN. CoRR Mahmoud Hossam, Trung Le, Michael Papasimeon, Viet Huynh, Dinh Phung
2021	J	`jnl`	Understanding and Achieving Efficient Robustness with Adversarial Contrastive Learning. CoRR Tuan-Anh Bui, Trung Le, He Zhao, Paul Montague, Seyit Camtepe, Dinh Phung
2020	—	`conf`	Code Action Network for Binary Function Scope Identification. PAKDD (1) Van Nguyen, Trung Le, Tue Le, Khanh Nguyen, Olivier Y. de Vel, Paul Montague, John C. Grundy, Dinh Phung
2020	B	`conf`	Code Pointer Network for Binary Function Scope Identification. IJCNN Van Nguyen, Trung Le, Tue Le, Khanh Nguyen, Olivier Y. de Vel, Paul Montague, Dinh Phung
2020	—	`conf`	Deep Cost-Sensitive Kernel Machine for Binary Software Vulnerability Detection. PAKDD (2) Tuan Nguyen, Trung Le, Khanh Nguyen, Olivier Y. de Vel, Paul Montague, John C. Grundy, Dinh Phung
2020	—	`conf`	Dual-Component Deep Domain Adaptation: A New Approach for Cross Project Software Vulnerability Detection. PAKDD (1) Van Nguyen, Trung Le, Olivier Y. de Vel, Paul Montague, John C. Grundy, Dinh Phung
2020	B	`conf`	Explain by Evidence: An Explainable Memory-based Neural Network for Question Answering. COLING Quan Hung Tran, Nhan Dam, Tuan Manh Lai, Franck Dernoncourt, Trung Le, Nham Le, Dinh Phung
2020	J	`jnl`	Explain by Evidence: An Explainable Memory-based Neural Network for Question Answering. CoRR Quan Hung Tran, Nhan Dam, Tuan Manh Lai, Franck Dernoncourt, Trung Le, Nham Le, Dinh Phung
2020	B	`conf`	Explain2Attack: Text Adversarial Attacks via Cross-Domain Interpretability. ICPR Mahmoud Hossam, Trung Le, He Zhao, Dinh Phung
2020	J	`jnl`	Explain2Attack: Text Adversarial Attacks via Cross-Domain Interpretability. CoRR Mahmoud Hossam, Trung Le, He Zhao, Dinh Phung
2020	—	`conf`	Improving Adversarial Robustness by Enforcing Local and Global Compactness. ECCV (27) Tuan-Anh Bui, Trung Le, He Zhao, Paul Montague, Olivier Y. DeVel, Tamas Abraham, Dinh Q. Phung
2020	J	`jnl`	Improving Adversarial Robustness by Enforcing Local and Global Compactness. CoRR Tuan-Anh Bui, Trung Le, He Zhao, Paul Montague, Olivier Y. DeVel, Tamas Abraham, Dinh Phung
2020	J	`jnl`	Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial Robustness. CoRR Tuan-Anh Bui, Trung Le, He Zhao, Paul Montague, Olivier Y. DeVel, Tamas Abraham, Dinh Phung
2020	J	`jnl`	Neural Sinkhorn Topic Model. CoRR He Zhao, Dinh Phung, Viet Huynh, Trung Le, Wray L. Buntine
2020	B	`conf`	OptiGAN: Generative Adversarial Networks for Goal Optimized Sequence Generation. IJCNN Mahmoud Hossam, Trung Le, Viet Huynh, Michael Papasimeon, Dinh Phung
2020	J	`jnl`	OptiGAN: Generative Adversarial Networks for Goal Optimized Sequence Generation. CoRR Mahmoud Hossam, Trung Le, Viet Huynh, Michael Papasimeon, Dinh Phung
2020	A*	`conf`	Parameterized Rate-Distortion Stochastic Encoder. ICML Quan Hoang, Trung Le, Dinh Phung
2020	B	`conf`	Stein Variational Gradient Descent with Variance Reduction. IJCNN Nhan Dam, Trung Le, Viet Huynh, Dinh Phung
2020	J	`jnl`	Towards Understanding Pixel Vulnerability under Adversarial Attacks for Images. CoRR He Zhao, Trung Le, Paul Montague, Olivier Y. de Vel, Tamas Abraham, Dinh Phung
2019	B	`conf`	Deep Domain Adaptation for Vulnerable Code Function Identification. IJCNN Van Nguyen, Trung Le, Tue Le, Khanh Nguyen, Olivier Y. DeVel, Paul Montague, Lizhen Qu, Dinh Q. Phung
2019	J	`jnl`	GoGP: scalable geometric-based Gaussian process for online regression. Knowl. Inf. Syst. Trung Le, Khanh Nguyen, Vu Nguyen, Tu Dinh Nguyen, Dinh Q. Phung
2019	A*	`conf`	Learning Generative Adversarial Networks from Multiple Data Sources. IJCAI Trung Le, Quan Hoang, Hung Vu, Tu Dinh Nguyen, Hung Bui, Dinh Q. Phung
2019	—	`conf`	Maximal Divergence Sequential Autoencoder for Binary Software Vulnerability Detection. ICLR (Poster) Tue Le, Tuan Nguyen, Trung Le, Dinh Q. Phung, Paul Montague, Olivier Y. de Vel, Lizhen Qu
2019	J	`jnl`	Perturbations are not Enough: Generating Adversarial Examples with Spatial Distortions. CoRR He Zhao, Trung Le, Paul Montague, Olivier Y. de Vel, Tamas Abraham, Dinh Phung
2019	A*	`conf`	Robust Anomaly Detection in Videos Using Multilevel Representations. AAAI Hung Vu, Tu Dinh Nguyen, Trung Le, Wei Luo, Dinh Q. Phung
2019	A*	`conf`	Three-Player Wasserstein GAN via Amortised Duality. IJCAI Nhan Dam, Quan Hoang, Trung Le, Tu Dinh Nguyen, Hung Bui, Dinh Phung
2019	J	`jnl`	When Can Neural Networks Learn Connected Decision Regions? CoRR Trung Le, Dinh Q. Phung
2018	C	`conf`	Batch Normalized Deep Boltzmann Machines. ACML Hung Vu, Tu Dinh Nguyen, Trung Le, Wei Luo, Dinh Q. Phung
2018	B	`conf`	Bayesian Multi-Hyperplane Machine for Pattern Recognition. ICPR Khanh Nguyen, Trung Le, Tu Dinh Nguyen, Dinh Q. Phung
2018	C	`conf`	Clustering Induced Kernel Learning. ACML Khanh Nguyen, Nhan Dam, Trung Le, Tu Dinh Nguyen, Dinh Q. Phung
2018	A*	`conf`	Geometric Enclosing Networks. IJCAI Trung Le, Hung Vu, Tu Dinh Nguyen, Dinh Q. Phung
2018	—	`conf`	Jointly Predicting Affective and Mental Health Scores Using Deep Neural Networks of Visual Cues on the Web. WISE (2) Hung Nguyen, Van Nguyen, Thin Nguyen, Mark E. Larsen, Bridianne O'Dea, Duc Thanh Nguyen, Trung Le, Dinh Q. Phung, Svetha Venkatesh, Helen Christensen
2018	—	`conf`	MGAN: Training Generative Adversarial Nets with Multiple Generators. ICLR (Poster) Quan Hoang, Tu Dinh Nguyen, Trung Le, Dinh Q. Phung
2018	A*	`conf`	Robust Bayesian Kernel Machine via Stein Variational Gradient Descent for Big Data. KDD Khanh Nguyen, Trung Le, Tu Dinh Nguyen, Dinh Q. Phung, Geoffrey I. Webb
2018	J	`jnl`	Theoretical Perspective of Deep Domain Adaptation. CoRR Trung Le, Khanh Nguyen, Dinh Q. Phung
2017	J	`jnl`	Analogical-based Bayesian Optimization. CoRR Trung Le, Khanh Nguyen, Tu Dinh Nguyen, Dinh Q. Phung
2017	J	`jnl`	Approximation Vector Machines for Large-scale Online Learning. J. Mach. Learn. Res. Trung Le, Tu Dinh Nguyen, Vu Nguyen, Dinh Q. Phung
2017	A*	`conf`	Discriminative Bayesian Nonparametric Clustering. IJCAI Vu Nguyen, Dinh Q. Phung, Trung Le, Hung Bui
2017	—	`conf`	Dual Discriminator Generative Adversarial Nets. NIPS Tu Dinh Nguyen, Trung Le, Hung Vu, Dinh Q. Phung
2017	J	`jnl`	Dual Discriminator Generative Adversarial Nets. CoRR Tu Dinh Nguyen, Trung Le, Hung Vu, Dinh Q. Phung
2017	J	`jnl`	Fast support vector clustering. Vietnam. J. Comput. Sci. Tung Pham, Hang Dang, Trung Le, Thai Hoang Le
2017	J	`jnl`	Geometric Enclosing Networks. CoRR Trung Le, Hung Vu, Tu Dinh Nguyen, Dinh Q. Phung
2017	A*	`conf`	GoGP: Fast Online Regression with Gaussian Processes. ICDM Trung Le, Khanh Nguyen, Vu Nguyen, Tu Dinh Nguyen, Dinh Q. Phung
2017	J	`jnl`	KGAN: How to Break The Minimax Game in GAN. CoRR Trung Le, Tu Dinh Nguyen, Dinh Q. Phung
2017	A*	`conf`	Large-scale Online Kernel Learning with Random Feature Reparameterization. IJCAI Tu Dinh Nguyen, Trung Le, Hung Bui, Dinh Q. Phung
2017	J	`jnl`	Multi-Generator Generative Adversarial Nets. CoRR Quan Hoang, Tu Dinh Nguyen, Trung Le, Dinh Q. Phung
2017	J	`jnl`	Scalable Support Vector Clustering Using Budget. CoRR Tung Pham, Trung Le, Hang Dang
2017	A	`conf`	Supervised Restricted Boltzmann Machines. UAI Tu Dinh Nguyen, Dinh Q. Phung, Viet Huynh, Trung Le
2016	J	`jnl`	Approximation Vector Machines for Large-scale Online Learning. CoRR Trung Le, Tu Dinh Nguyen, Vu Nguyen, Dinh Q. Phung
2016	A	`conf`	Budgeted Semi-supervised Support Vector Machine . UAI Trung Le, Phuong Duong, Mi Dinh, Tu Dinh Nguyen, Vu Nguyen, Dinh Q. Phung
2016	B	`conf`	Distributed data augmented support vector machine on Spark. ICPR Tu Dinh Nguyen, Vu Nguyen, Trung Le, Dinh Q. Phung
2016	—	`conf`	Dual Space Gradient Descent for Online Learning. NIPS Trung Le, Tu Dinh Nguyen, Vu Nguyen, Dinh Q. Phung
2016	B	`conf`	Fast Kernel-based method for anomaly detection. IJCNN Anh Le, Trung Le, Khanh Nguyen, Van Nguyen, Thai Hoang Le, Dat Tran
2016	B	`conf`	Fast Support Vector Clustering. ESANN Tung Pham, Trung Le, Thai Hoang Le, Dat Tran
2016	B	`conf`	Fuzzy Kernel Stochastic Gradient Descent machines. IJCNN Tuan Nguyen, Phuong Duong, Trung Le, Anh Le, Viet Ngo, Dat Tran, Wanli Ma
2016	J	`jnl`	Mixture of hyperspheres for novelty detection. Vietnam. J. Comput. Sci. Duy Nguyen, Vinh Lai, Khanh Nguyen, Trung Le
2016	C	`conf`	Multiple Kernel Learning with Data Augmentation. ACML Khanh Nguyen, Trung Le, Vu Nguyen, Tu Dinh Nguyen, Dinh Q. Phung
2016	A	`conf`	Nonparametric Budgeted Stochastic Gradient Descent. AISTATS Trung Le, Vu Nguyen, Tu Dinh Nguyen, Dinh Q. Phung
2016	A*	`conf`	One-Pass Logistic Regression for Label-Drift and Large-Scale Classification on Distributed Systems. ICDM Vu Nguyen, Tu Dinh Nguyen, Trung Le, Svetha Venkatesh, Dinh Q. Phung
2016	J	`jnl`	Scalable Support Vector Machine for Semi-supervised Learning. CoRR Trung Le, Khanh Nguyen, Van Nguyen, Vu Nguyen, Dinh Q. Phung
2016	—	`conf`	Sparse Adaptive Multi-hyperplane Machine. PAKDD (1) Khanh Nguyen, Trung Le, Vu Nguyen, Dinh Q. Phung
2015	—	`conf`	Fast One-Class Support Vector Machine for Novelty Detection. PAKDD (2) Trung Le, Dinh Q. Phung, Khanh Nguyen, Svetha Venkatesh
2015	B	`conf`	Graph-based semi-supervised Support Vector Data Description for novelty detection. IJCNN Phuong Duong, Van Nguyen, Mi Dinh, Trung Le, Dat Tran, Wanli Ma
2015	B	`conf`	Least square Support Vector Machine for large-scale dataset. IJCNN Khanh Nguyen, Trung Le, Vinh Lai, Duy Nguyen, Dat Tran, Wanli Ma
2014	B	`conf`	Kernel-based semi-supervised learning for novelty detection. IJCNN Van Nguyen, Trung Le, Thien Pham, Mi Dinh, Thai Hoang Le
2014	B	`conf`	Robust Support Vector Machine. IJCNN Trung Le, Dat Tran, Wanli Ma, Thien Pham, Phuong Duong, Minh Nguyen
2014	B	`conf`	Using EEG artifacts for BCI applications. IJCNN Wanli Ma, Dat Tran, Trung Le, Hong Lin, Shang-Ming Zhou
2013	—	`conf`	EEG-Based Person Verification Using Multi-Sphere SVDD and UBM. PAKDD (1) Phuoc Nguyen, Dat Tran, Trung Le, Xu Huang, Wanli Ma
2013	—	`conf`	Fuzzy Multi-Sphere Support Vector Data Description. PAKDD (2) Trung Le, Dat Tran, Wanli Ma
2013	B	`conf`	Fuzzy entropy semi-supervised support vector data description. IJCNN Trung Le, Dat Tran, Tien Tran, Khanh Nguyen, Wanli Ma
2013	B	`conf`	Maximal margin learning vector quantisation. IJCNN Trung Le, Dat Tran, Van Nguyen, Wanli Ma
2013	J	`jnl`	Proximity multi-sphere support vector clustering. Neural Comput. Appl. Trung Le, Dat Tran, Phuoc Nguyen, Wanli Ma, Dharmendra Sharma
2012	B	`conf`	A unified model for support vector machine and support vector data description. IJCNN Trung Le, Dat Tran, Wanli Ma, Dharmendra Sharma
2012	—	`conf`	Deterministic Annealing Multi-Sphere Support Vector Data Description. ICONIP (3) Trung Le, Dat Tran, Wanli Ma, Dharmendra Sharma
2012	B	`conf`	Fuzzy Multi-sphere Support Vector Data Description. FUZZ-IEEE Trung Le, Dat Tran, Wanli Ma, Dharmendra Sharma
2012	—	`conf`	Maximal Margin Approach to Kernel Generalised Learning Vector Quantisation for Brain-Computer Interface. ICONIP (3) Trung Le, Dat Tran, Tuan Hoang, Dharmendra Sharma
2012	—	`conf`	Time Domain Parameters for Online Feedback fNIRS-Based Brain-Computer Interface Systems. ICONIP (2) Tuan Hoang, Dat Tran, Khoa Truong, Trung Le, Xu Huang, Dharmendra Sharma, Toi Vo
2011	—	`conf`	A Novel Parameter Refinement Approach to One Class Support Vector Machine. ICONIP (2) Trung Le, Dat Tran, Wanli Ma, Dharmendra Sharma
2011	—	`conf`	Generalised Support Vector Machine for Brain-Computer Interface. ICONIP (1) Trung Le, Dat Tran, Tuan Hoang, Wanli Ma, Dharmendra Sharma
2011	—	`conf`	Multi-Sphere Support Vector Clustering. ICONIP (2) Trung Le, Dat Tran, Phuoc Nguyen, Wanli Ma, Dharmendra Sharma
2011	—	`conf`	Multiple Distribution Data Description Learning Algorithm for Novelty Detection. PAKDD (2) Trung Le, Dat Tran, Wanli Ma, Dharmendra Sharma
2011	B	`conf`	Multiple distribution data description learning method for novelty detection. IJCNN Trung Le, Dat Tran, Phuoc Nguyen, Wanli Ma, Dharmendra Sharma
2010	—	`conf`	A Theoretical Framework for Multi-sphere Support Vector Data Description. ICONIP (2) Trung Le, Dat Tran, Wanli Ma, Dharmendra Sharma
2010	—	`conf`	A new support vector machine method for medical image classification. EUVIP Trung Le, Dat Tran, Wanli Ma, Dharmendra Sharma
2010	B	`conf`	An optimal sphere and two large margins approach for novelty detection. IJCNN Trung Le, Dat Tran, Wanli Ma, Dharmendra Sharma
2010	J	`jnl`	Enhancing Performance of SVM-Based Brain-Computer Interface Systems. Aust. J. Intell. Inf. Process. Syst. Tuan Hoang, Phuoc Nguyen, Trung Le, Dat Tran, Dharmendra Sharma
2010	A	`conf`	Fuzzy support vector machines for age and gender classification. INTERSPEECH Phuoc Nguyen, Trung Le, Dat Tran, Xu Huang, Dharmendra Sharma